Spectrum estimation, that is, analysis of the frequency content of a signal, is a core operation in numerous applications, such as data compression, medical data analysis (ECG data), pitch detection of musical content, and other applications. Widely used estimators of the frequency content are the periodogram and the autocorrelation of a sequence. For statically stored sequences, both methods have an O(nlogn) complexity using the Fast Fourier Transform (FFT). For dynamically updated sequences (streaming case), the same estimators can be computed incrementally, by continuous update of the summation in the FFT computation, through the use of Momentary Fourier Transform.
However, in a high-rate, data streaming environment with multiple processes ‘competing’ over computational resources, there is no guarantee that each running process will be allotted sufficient processing time to fully complete its operation. Instead of blocking or abandoning the execution of processing threads that cannot fully complete, a desirable compromise would be for the system to make provisions for adaptive process computation. Under this processing model every analytic unit (e.g., in this case the ‘periodogram estimation unit’) can provide partial (coarser') results under tight processing constraints.
Under the aforementioned processing model and given limited processing time, one may not be seeking for results that are accurate or perfect, but only ‘good enough’. Even so, since a typical streaming application will require fast, ‘on-the-fly’ decisions, an intelligent sampling procedure of exemplary efficiency would appear to represent a significant improvement over conventional efforts. A need has thus been recognized in connection with effecting such an improvement, among others.